Abstract
Loss of vision in the present era of the developing world is mainly caused by diabetic retinopathy. More than 103 million people are believed to be affected. It is estimated that around 40 million beings have diabetes in the United States, and according to the World Health Organization (WHO), 347 million people are living with the disease globally. Diabetic retinopathy (DR) is a long-term diabetes-related eye condition. Roughly, 45–50% of the American citizens suffering from diabetes undergo some unique stages that can be categorized. When DR is diagnosed on a timely basis, the possibility of it extending to the course of vision impairment can be delayed and stopped, though this is not entirely true and a very daunting task because it seldom reveals any symptom before it escalates to a stage of no return to effectively treat it. The paper uses convolutional neural network models to achieve an effective classification for diabetic detection of retinal fundus images.
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Acknowledgements
We would like to express our gratitude toward the Information Technology Department of NITK, Surathkal for its kind cooperation and encouragement that helped us in the completion of this project entitled “An Effective Diabetic Retinopathy Detection using Hybrid Convolutional Neural Network Models.” We would like to thank the department for providing the necessary cluster and GPU technology to implement the project in a preferable environment. We are grateful for the guidance and constant supervision as well as for providing necessary information regarding the project and also for its support in completing the project.
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Kumar, N., Ahmed, R., Venkatesh, B.H., Anand Kumar, M. (2023). An Effective Diabetic Retinopathy Detection Using Hybrid Convolutional Neural Network Models. In: Kumar, B.V., Sivakumar, P., Surendiran, B., Ding, J. (eds) Smart Computer Vision. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-20541-5_14
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